Sm. Lin et al., SELF-ORGANIZATION OF FIRING ACTIVITIES IN MONKEYS MOTOR CORTEX - TRAJECTORY COMPUTATION FROM SPIKE SIGNALS, Neural computation, 9(3), 1997, pp. 607-621
The population vector method has been developed to combine the simulta
neous direction-related activities of a population of motor cortical n
eurons to predict the trajectory of the arm movement. In this article,
we consider a self-organizing model of a neural representation of the
arm trajectory based on neuronal discharge rates. A self-organizing f
eature map (SOFM) is used to select the optimal set of weights in the
model to determine the contribution of an individual neuron to an over
all movement representation. The correspondence between movement direc
tions and discharge patterns of the motor cortical neurons is establis
hed in the output map. The topology-preserving property of the SOFM is
used to analyze the recorded data of a behaving monkey. The data used
in this analysis were taken while the monkey was tracing spirals and
doing center --> out movements. The arm trajectory could be well predi
cted using such a statistical model based on the motor cortex neuronal
firing information. The SOFM method is compared with the population v
ector method, which extracts information related to trajectory by assu
ming that each cell has a fixed preferred direction during the task. T
his implies that these cells are acting along lines labeled only for d
irection. However, extradirectional information is carried in these ce
ll responses. The SOFM has the capability of extracting not only direc
tion-related information but also other parameters that are consistent
ly represented in the activity of the recorded population of cells.